Doctors' Perceptions of Artificial Intelligence in Managing Diabetes during Ramadan: An Exploratory Cross-Sectional Survey
Bibliographic record
Abstract
Abstract Ramadan fasting (RF) presents unique challenges for people with diabetes. Artificial intelligence (AI) has the potential to enhance safety and personalize care, but little is known about doctors' readiness to adopt such tool in this context. This article explores doctors' knowledge, attitudes, and practices regarding the use of AI in managing diabetes during Ramadan. An online exploratory cross-sectional survey of a convenience sample of 134 doctors was conducted between July 18 and August 31, 2025, using a structured questionnaire distributed through professional networks interested in RF. Items assessed demographics, familiarity with AI, clinical attitudes, and perceived barriers to the use of AI. Descriptive analyses were performed; no hypothesis testing was undertaken. Of 134 respondents, 60.4% were endocrinologists and 74.6% were senior consultants. While 62.7% had received Ramadan-specific diabetes training, only 23.9% had training in AI. Familiarity was highest with continuous glucose monitoring tools (55.2%) and automated insulin delivery systems (35.1%), yet 38.8% reported no knowledge of AI applications. Although 73.9% agreed AI could enhance safety during fasting, only 48.5% felt confident using AI for decision-making. Barriers included affordability (59.7%), limited access (56.0%), and lack of training (54.5%). Over a quarter of respondents perceived clinical benefits. Most respondents (69.4%) expressed interest in AI training. Doctors recognize AI's potential to support safe fasting but face substantial knowledge and training gaps. Structured education, improved access, and culturally sensitive integration are urgently needed to enable wider adoption of AI in Ramadan-focused diabetes care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".